






from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_torch_available():
 import torch

 from transformers.modeling_outputs import BaseModelOutput

 from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

snake_case__   :						int													=   logging.get_logger(__name__)


@add_end_docstrings(UpperCAmelCase__     )
class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def __init__(  self		,		**_UpperCAmelCase			)      ->			Union[str, Any]:
     super().__init__(**_UpperCAmelCase			)

     if self.framework == "tf":
        raise ValueError(f"""The {self.__class__} is only available in PyTorch."""			)

     requires_backends(self		,		'vision'			)
     self.check_model_type(_UpperCAmelCase			)




  def __call__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)      ->			Union[str, Any]:
     if "text_queries" in kwargs:
        UpperCamelCase_						=  kwargs.pop('text_queries'			)

     if isinstance(_UpperCAmelCase		,		(str, Image.Image)			):
        UpperCamelCase_						=  {'image': image, 'candidate_labels': candidate_labels}
     else:
        UpperCamelCase_						=  image
     UpperCamelCase_						=  super().__call__(_UpperCAmelCase		,		**_UpperCAmelCase			)
     return results




  def  _UpperCAmelCase  (  self		,		**_UpperCAmelCase			)      ->			Optional[int]:
     UpperCamelCase_						=  {}
     if "threshold" in kwargs:
        UpperCamelCase_						=  kwargs['threshold']
     if "top_k" in kwargs:
        UpperCamelCase_						=  kwargs['top_k']
     return {}, {}, postprocess_params




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Optional[Any]:
     UpperCamelCase_						=  load_image(inputs['image']			)
     UpperCamelCase_						=  inputs['candidate_labels']
     if isinstance(_UpperCAmelCase		,		_UpperCAmelCase			):
        UpperCamelCase_						=  candidate_labels.split(','			)

     UpperCamelCase_						=  torch.tensor([[image.height, image.width]]		,		dtype=torch.intaa			)
     for i, candidate_label in enumerate(_UpperCAmelCase			):
        UpperCamelCase_						=  self.tokenizer(_UpperCAmelCase		,		return_tensors=self.framework			)
        UpperCamelCase_						=  self.image_processor(_UpperCAmelCase		,		return_tensors=self.framework			)
        yield {
            "is_last": i == len(_UpperCAmelCase			) - 1,
            "target_size": target_size,
            "candidate_label": candidate_label,
            **text_inputs,
            **image_features,
        }




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			str:
     UpperCamelCase_						=  model_inputs.pop('target_size'			)
     UpperCamelCase_						=  model_inputs.pop('candidate_label'			)
     UpperCamelCase_						=  model_inputs.pop('is_last'			)

     UpperCamelCase_						=  self.model(**_UpperCAmelCase			)

     UpperCamelCase_						=  {'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
     return model_outputs




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=None			)      ->			List[str]:
     UpperCamelCase_						=  []
     for model_output in model_outputs:
        UpperCamelCase_						=  model_output['candidate_label']
        UpperCamelCase_						=  BaseModelOutput(_UpperCAmelCase			)
        UpperCamelCase_						=  self.image_processor.post_process_object_detection(
            outputs=_UpperCAmelCase		,		threshold=_UpperCAmelCase		,		target_sizes=model_output['target_size']			)[0]

        for index in outputs["scores"].nonzero():
           UpperCamelCase_						=  outputs['scores'][index].item()
           UpperCamelCase_						=  self._get_bounding_box(outputs['boxes'][index][0]			)

           UpperCamelCase_						=  {'score': score, 'label': label, 'box': box}
           results.append(_UpperCAmelCase			)

     UpperCamelCase_						=  sorted(_UpperCAmelCase		,		key=lambda _UpperCAmelCase			: x["score"]		,		reverse=_UpperCAmelCase			)
     if top_k:
        UpperCamelCase_						=  results[:top_k]

     return results




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Dict[str, int]:
     if self.framework != "pt":
        raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.'			)
     UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_						=  box.int().tolist()
     UpperCamelCase_						=  {
         'xmin': xmin,
         'ymin': ymin,
         'xmax': xmax,
         'ymax': ymax,
     }
     return bbox













"""simple docstring"""
import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


def 							A						(				__snake_case: Tuple  )    ->		Optional[Any]:

       """simple docstring"""





       if (
           (cp >= 0x4_E00 and cp <= 0x9_FFF)
           or (cp >= 0x3_400 and cp <= 0x4_DBF)  #
           or (cp >= 0x20_000 and cp <= 0x2A_6DF)  #
           or (cp >= 0x2A_700 and cp <= 0x2B_73F)  #
           or (cp >= 0x2B_740 and cp <= 0x2B_81F)  #
           or (cp >= 0x2B_820 and cp <= 0x2C_EAF)  #
           or (cp >= 0xF_900 and cp <= 0xF_AFF)
           or (cp >= 0x2F_800 and cp <= 0x2F_A1F)  #
       ):  #
              return True

       return False


def 							A						(				__snake_case: str  )    ->		Optional[Any]:

       """simple docstring"""





       for char in word:
              __magic_name__      				=    ord(__snake_case  )
              if not _is_chinese_char(__snake_case  ):
                     return 0
       return 1


def 							A						(				__snake_case: List[str]  )    ->		List[str]:

       """simple docstring"""





       __magic_name__      				=    set()

       for token in tokens:
              __magic_name__      				=    len(__snake_case  ) > 1 and is_chinese(__snake_case  )
              if chinese_word:
                     word_set.add(__snake_case  )
       __magic_name__      				=    list(__snake_case  )
       return word_list


def 							A						(				__snake_case: List[str] ,     __snake_case: set()  )    ->		str:

       """simple docstring"""





       if not chinese_word_set:
              return bert_tokens
       __magic_name__      				=    max([len(__snake_case  ) for w in chinese_word_set]  )

       __magic_name__      				=    bert_tokens
       __magic_name__				,  __magic_name__      				=    0, len(__snake_case  )
       while start < end:
              __magic_name__      				=    True
              if is_chinese(bert_word[start]  ):
                     __magic_name__      				=    min(end - start ,     __snake_case  )
                     for i in range(__snake_case ,     1 ,     -1  ):
                            __magic_name__      				=    ''.join(bert_word[start : start + i]  )
                            if whole_word in chinese_word_set:
                                   for j in range(start + 1 ,     start + i  ):
                                          __magic_name__      				=    '##' + bert_word[j]
                                   __magic_name__      				=    start + i
                                   __magic_name__      				=    False
                                   break
              if single_word:
                     start += 1
       return bert_word


def 							A						(				__snake_case: List[str] ,     __snake_case: LTP ,     __snake_case: BertTokenizer  )    ->		List[Any]:

       """simple docstring"""





       __magic_name__      				=    []

       for i in range(0 ,     len(__snake_case  ) ,     1_0_0  ):
              __magic_name__      				=    ltp_tokenizer.pipeline(lines[i : i + 1_0_0] ,     tasks=['cws']  ).cws
              __magic_name__      				=    [get_chinese_word(__snake_case  ) for r in res]
              ltp_res.extend(__snake_case  )
       assert len(__snake_case  ) == len(__snake_case  )

       __magic_name__      				=    []
       for i in range(0 ,     len(__snake_case  ) ,     1_0_0  ):
              __magic_name__      				=    bert_tokenizer(lines[i : i + 1_0_0] ,     add_special_tokens=__snake_case ,     truncation=__snake_case ,     max_length=5_1_2  )
              bert_res.extend(res['input_ids']  )
       assert len(__snake_case  ) == len(__snake_case  )

       __magic_name__      				=    []
       for input_ids, chinese_word in zip(__snake_case ,     __snake_case  ):
              __magic_name__      				=    []
              for id in input_ids:
                     __magic_name__      				=    bert_tokenizer._convert_id_to_token(__snake_case  )
                     input_tokens.append(__snake_case  )
              __magic_name__      				=    add_sub_symbol(__snake_case ,     __snake_case  )
              __magic_name__      				=    []
              # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
              for i, token in enumerate(__snake_case  ):
                     if token[:2] == "##":
                            __magic_name__      				=    token[2:]
                            # save chinese tokens' pos
                            if len(__snake_case  ) == 1 and _is_chinese_char(ord(__snake_case  )  ):
                                   ref_id.append(__snake_case  )
              ref_ids.append(__snake_case  )

       assert len(__snake_case  ) == len(__snake_case  )

       return ref_ids


def 							A						(				__snake_case: Optional[Any]  )    ->		List[str]:

       """simple docstring"""





       with open(args.file_name ,     'r' ,     encoding='utf-8'  ) as f:
              __magic_name__      				=    f.readlines()
       __magic_name__      				=    [line.strip() for line in data if len(__snake_case  ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
       __magic_name__      				=    LTP(args.ltp  )  # faster in GPU device
       __magic_name__      				=    BertTokenizer.from_pretrained(args.bert  )

       __magic_name__      				=    prepare_ref(__snake_case ,     __snake_case ,     __snake_case  )

       with open(args.save_path ,     'w' ,     encoding='utf-8'  ) as f:
              __magic_name__      				=    [json.dumps(__snake_case  ) + '\n' for ref in ref_ids]
              f.writelines(__snake_case  )


if __name__ == "__main__":
 snake_case : Tuple     		=		argparse.ArgumentParser(description="""prepare_chinese_ref""")
 parser.add_argument(
     """--file_name""",
     required=False,
     type=str,
     default="""./resources/chinese-demo.txt""",
     help="""file need process, same as training data in lm""",
 )
 parser.add_argument(
     """--ltp""",
     required=False,
     type=str,
     default="""./resources/ltp""",
     help="""resources for LTP tokenizer, usually a path""",
 )
 parser.add_argument(
     """--bert""",
     required=False,
     type=str,
     default="""./resources/robert""",
     help="""resources for Bert tokenizer""",
 )
 parser.add_argument(
     """--save_path""",
     required=False,
     type=str,
     default="""./resources/ref.txt""",
     help="""path to save res""",
 )

 snake_case : Any     		=		parser.parse_args()
 main(args)